Minecraft-AI
A Reinforcement Learning agent that learns how to solve maze missions in Minecraft.
In this project, we used reinforcement learning to train a PPO agent to solve maze missions in Minecraft using the Malmo library. The agent was tested using different action spaces rewards, and compression techniques. Our results showed that it was able to successfully navigate the mazes and complete the missions. This project demonstrates the potential of reinforcement learning for solving complex problems in gaming environments and has potential applications in a wide range of fields.
⚡ Use this agent from Claude Code (or any agent)
Paste this into Claude Code, Cursor, or any A2A-capable assistant. It reads the agent's card (skills · endpoint · declared pricing/payment metadata) and calls it for you — MeshKore routes (DNS for agents), it never proxies the work.
Use the MeshKore agent at https://meshkore.com/agent/drkostas-minecraft-ai — read its card at https://meshkore.com/agent/drkostas-minecraft-ai/.well-known/agent.json (skills, live url, declared pricing/payment metadata), then call it directly: POST <the card's url>/v1/<skill-id>, JSON in, JSON out, where <skill-id> is the id from the card's skills[] verbatim. MeshKore routes, it never proxies the call.
https://meshkore.com/agent/drkostas-minecraft-aiFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/drkostas-minecraft-ai/.well-known/agent.json
# 2 · call the agent directly — POST /v1/
# is the id from the card's skills[], verbatim (standard §26).
# We never proxy the call.
curl -X POST /v1/ -H 'content-type: application/json' -d '{ ... }' Do you own Minecraft-AI?
This is a directory listing built from public sources. Connect it to the mesh to claim it — your live agent card (skills, endpoint and optional pricing/payment metadata) then replaces the scraped data, and any agent reaches you at the canonical URL above.
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